Problem-Based Learning e suas implicações: breve revisão teórica

A ESTSP-IPP implementou em 2008-2009 um novo modelo pedagógico, o PBL, em três licenciaturas. Este modelo tem sido considerado capaz de promover a aquisição de conhecimentos mas também o desenvolvimento de competências transversais valorizadas no mercado de trabalho; orienta-se em torno de problemas significativos da realidade profissional, trabalhados segundo a metodologia dos sete passos, destacando-se a aprendizagem através de pesquisa individual e trabalho de grupo; e visa ainda desenvolver processos cognitivos e metacognitivos como levantar hipóteses, comparar, analisar, interpretar e avaliar. Neste artigo, caracterizamos brevemente o modelo e respectivas implicações, justificando o interesse em investigar as repercussões da sua implementação.

Ant colony system based approach to single machine scheduling problems: weighted tardiness scheduling problem

The paper introduces an approach to solve the problem of generating a sequence of jobs that minimizes the total weighted tardiness for a set of jobs to be processed in a single machine. An Ant Colony System based algorithm is validated with benchmark problems available in the OR library. The obtained results were compared with the best available results and were found to be nearer to the optimal. The obtained computational results allowed concluding on their efficiency and effectiveness.

Multi-apprentice learning for meta-heuristics parameter tuning in a multi agent scheduling system

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

Meta-heuristics tunning using CBR for dynamic scheduling

This paper addresses the problem of Biological Inspired Optimization Techniques (BIT) parameterization, considering the importance of this issue in the design of BIT especially when considering real world situations, subject to external perturbations. A learning module with the objective to permit a Multi-Agent Scheduling System to automatically select a Meta-heuristic and its parameterization to use in the optimization process is proposed. For the learning process, Casebased Reasoning was used, allowing the system to learn from experience, in the resolution of similar problems. Analyzing the obtained results we conclude about the advantages of its use.

Self-managing agents for dynamic scheduling in manufacturing

The main purpose of this paper is to propose a Multi-Agent Autonomic and Bio-Inspired based framework with selfmanaging capabilities to solve complex scheduling problems using cooperative negotiation. Scheduling resolution requires the intervention of highly skilled human problem-solvers. This is a very hard and challenging domain because current systems are becoming more and more complex, distributed, interconnected and subject to rapidly changing. A natural Autonomic Computing (AC) evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference.

Context-aware emotion-based model for group decision making

Involving groups in important management processes such as decision making has several advantages. By discussing and combining ideas, counter ideas, critical opinions, identified constraints, and alternatives, a group of individuals can test potentially better solutions, sometimes in the form of new products, services, and plans. In the past few decades, operations research, AI, and computer science have had tremendous success creating software systems that can achieve optimal solutions, even for complex problems. The only drawback is that people don’t always agree with these solutions. Sometimes this dissatisfaction is due to an incorrect parameterization of the problem. Nevertheless, the reasons people don’t like a solution might not be quantifiable, because those reasons are often based on aspects such as emotion, mood, and personality. At the same time, monolithic individual decisionsupport systems centered on optimizing solutions are being replaced by collaborative systems and group decision-support systems (GDSSs) that focus more on establishing connections between people in organizations. These systems follow a kind of social paradigm. Combining both optimization- and socialcentered approaches is a topic of current research. However, even if such a hybrid approach can be developed, it will still miss an essential point: the emotional nature of group participants in decision-making tasks. We’ve developed a context-aware emotion based model to design intelligent agents for group decision-making processes. To evaluate this model, we’ve incorporated it in an agent-based simulator called ABS4GD (Agent-Based Simulation for Group Decision), which we developed. This multiagent simulator considers emotion- and argument based factors while supporting group decision-making processes. Experiments show that agents endowed with emotional awareness achieve agreements more quickly than those without such awareness. Hence, participant agents that integrate emotional factors in their judgments can be more successful because, in exchanging arguments with other agents, they consider the emotional nature of group decision making.

Solving MPCC problem with the hyperbolic penalty function

The main goal of this work is to solve mathematical program with complementarity constraints (MPCC) using nonlinear programming techniques (NLP). An hyperbolic penalty function is used to solve MPCC problems by including the complementarity constraints in the penalty term. This penalty function [1] is twice continuously differentiable and combines features of both exterior and interior penalty methods. A set of AMPL problems from MacMPEC [2] are tested and a comparative study is performed.

Solving a signalized traffic intersection problem with an hyperbolic penalty function

Mathematical Program with Complementarity Constraints (MPCC) finds many applications in fields such as engineering design, economic equilibrium and mathematical programming theory itself. A queueing system model resulting from a single signalized intersection regulated by pre-timed control in traffic network is considered. The model is formulated as an MPCC problem. A MATLAB implementation based on an hyperbolic penalty function is used to solve this practical problem, computing the total average waiting time of the vehicles in all queues and the green split allocation. The problem was codified in AMPL.

Numerical experiments with a modified regularization scheme for mathematical programs with complementarity constraints

On this paper we present a modified regularization scheme for Mathematical Programs with Complementarity Constraints. In the regularized formulations the complementarity condition is replaced by a constraint involving a positive parameter that can be decreased to zero. In our approach both the complementarity condition and the nonnegativity constraints are relaxed. An iterative algorithm is implemented in MATLAB language and a set of AMPL problems from MacMPEC database were tested.

Gestão e monitorização de uma rede departamental: um caso de estudo

Mestrado em Engenharia Electrotécnica e de Computadores

Supervisão, comando e controlo para sistemas robóticos

Mestrado em Engenharia Electrotécnica e de Computadores. Área de Especialização em Sistemas Autónomos

Análise de risco em sequenciamento de actividades com restrições de recursos

Mestrado em Engenharia Electrotécnica e de Computadores. Área de Especialização em Sistemas e Planeamento Industrial.

Sistema de percepção visual para veículos autónomos aéreos

Esta dissertação aborda o problema de detecção e desvio de obstáculos "SAA- Sense And Avoid" em movimento para veículos aéreos. Em particular apresenta contribuições tendo em vista a obtenção de soluções para permitir a utilização de aeronaves não tripuladas em espaço aéreo não segregado e para aplicações civis. Estas contribuições caracterizam-se por: uma análise do problema de SAA em \UAV's - Unmmaned Aerial Vehicles\ civis; a definição do conceito e metodologia para o projecto deste tipo de sistemas; uma proposta de \ben- chmarking\ para o sistema SAA caracterizando um conjunto de "datasets\ adequados para a validação de métodos de detecção; respectiva validação experimental do processo e obtenção de "datasets"; a análise do estado da arte para a detecção de \Dim point features\ ; o projecto de uma arquitectura para uma solução de SAA incorporando a integração de compensação de \ego motion" e respectiva validação para um "dataset" recolhido. Tendo em vista a análise comparativa de diferentes métodos bem como a validação de soluções foi proposta a recolha de um conjunto de \datasets" de informação sensorial e de navegação. Para os mesmos foram definidos um conjunto de experiências e cenários experimentais. Foi projectado e implementado um setup experimental para a recolha dos \datasets" e realizadas experiências de recolha recorrendo a aeronaves tripuladas. O setup desenvolvido incorpora um sistema inercial de alta precisão, duas câmaras digitais sincronizadas (possibilitando análise de informa formação stereo) e um receptor GPS. As aeronaves alvo transportam um receptor GPS com logger incorporado permitindo a correlação espacial dos resultados de detecção. Com este sistema foram recolhidos dados referentes a cenários de aproximação com diferentes trajectórias e condições ambientais bem como incorporando movimento do dispositivo detector. O método proposto foi validado para os datasets recolhidos tendo-se verificado, numa análise preliminar, a detecção do obstáculo (avião ultraleve) em todas as frames para uma distância inferior a 3 km com taxas de sucesso na ordem dos 95% para distâncias entre os 3 e os 4 km. Os resultados apresentados permitem validar a arquitectura proposta para a solução do problema de SAA em veículos aéreos autónomos e abrem perspectivas muito promissoras para desenvolvimento futuro com forte impacto técnico-científico bem como sócio-economico. A incorporação de informa formação de \ego motion" permite fornecer um forte incremento em termos de desempenho.